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<p>Software-defined networks (SDN), owing to their centralized control architecture, provide high flexibility in network management, configuration, and monitoring; however, this architecture also introduces critical challenges related to scalability, performance bottlenecks, and quality of service (QoS) degradation under heavy and dynamic traffic conditions, particularly in large-scale and beyond 5G (B5G) networks with stringent real-time latency requirements. In such environments, the controller placement problem (CPP) becomes an inherently NP-hard multi-objective optimization task, where conventional sequential and heuristic methods struggle to explore the massive solution space within practical time constraints, thereby motivating the need for computationally scalable frameworks that can exploit parallel processing and high-performance computing (HPC) capabilities. To address these challenges, this paper proposes DeepWK-MSTC, an advanced multi-objective controller placement framework that integrates weighted Kmeans-based clustering with a deep learning-driven optimization mechanism. The proposed method leverages the inherent parallelism of Deep Monte Carlo Tree Search (Deep-MCTS) to enable concurrent rollouts and accelerated decision-making, while jointly optimizing three key objectives: minimizing average delay ratio (ADR), improving energy efficiency (EE), and balancing controller load under dynamic traffic patterns. By incorporating network topology characteristics and real-time traffic dynamics, DeepWK-MSTC efficiently avoids local optima and ensures stable optimization behavior. The effectiveness of the proposed framework is evaluated on six real-world network topologies from the Internet Topology Zoo, namely Aarnet, Chinanet, Deutsche Telekom, Colt, Cogent, and Tata, and compared against state-of-the-art baselines including ALO and ELA-RCP. Experimental results demonstrate that DeepWK-MSTC achieves an average reduction of 50.2% in ADR, an average energy saving of 26.45%, and a 24% decrease in maximum controller load, with an additional 11.5% relative ADR reduction compared specifically to ELA-RCP. Overall, by explicitly exploiting parallel optimization and HPC-oriented design principles, DeepWK-MSTC enhances resource utilization and ensures scalable, stable, and real-time-capable controller placement for large-scale SDN environments.</p>

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DeepWK-MSTC: a novel approach for adaptive controller placement in software-defined networks via deep learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月16日

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  • Rasoul Farahi,
  • Ali Ghaffari,
  • Nahideh Derakhshanfard,
  • Shiva Taghipoureivazi

摘要

Software-defined networks (SDN), owing to their centralized control architecture, provide high flexibility in network management, configuration, and monitoring; however, this architecture also introduces critical challenges related to scalability, performance bottlenecks, and quality of service (QoS) degradation under heavy and dynamic traffic conditions, particularly in large-scale and beyond 5G (B5G) networks with stringent real-time latency requirements. In such environments, the controller placement problem (CPP) becomes an inherently NP-hard multi-objective optimization task, where conventional sequential and heuristic methods struggle to explore the massive solution space within practical time constraints, thereby motivating the need for computationally scalable frameworks that can exploit parallel processing and high-performance computing (HPC) capabilities. To address these challenges, this paper proposes DeepWK-MSTC, an advanced multi-objective controller placement framework that integrates weighted Kmeans-based clustering with a deep learning-driven optimization mechanism. The proposed method leverages the inherent parallelism of Deep Monte Carlo Tree Search (Deep-MCTS) to enable concurrent rollouts and accelerated decision-making, while jointly optimizing three key objectives: minimizing average delay ratio (ADR), improving energy efficiency (EE), and balancing controller load under dynamic traffic patterns. By incorporating network topology characteristics and real-time traffic dynamics, DeepWK-MSTC efficiently avoids local optima and ensures stable optimization behavior. The effectiveness of the proposed framework is evaluated on six real-world network topologies from the Internet Topology Zoo, namely Aarnet, Chinanet, Deutsche Telekom, Colt, Cogent, and Tata, and compared against state-of-the-art baselines including ALO and ELA-RCP. Experimental results demonstrate that DeepWK-MSTC achieves an average reduction of 50.2% in ADR, an average energy saving of 26.45%, and a 24% decrease in maximum controller load, with an additional 11.5% relative ADR reduction compared specifically to ELA-RCP. Overall, by explicitly exploiting parallel optimization and HPC-oriented design principles, DeepWK-MSTC enhances resource utilization and ensures scalable, stable, and real-time-capable controller placement for large-scale SDN environments.